The present invention relates to a diagnosis and maintenance method, a diagnosis and maintenance assembly comprising a central server and a system, and a computer program for fault detection for a plurality of systems, particularly for a plurality of vehicles, wherein each system provides at least one system-related signal which serves as basis for the diagnosis and/or maintenance of/for the system.
Even if the invention will be described in the following more in detail in regard to its application to vehicles it is also usable for any other mechatronic or electronic system. The detailed description relates to a preferred embodiment, only, and shall in no way be understood to limit the scope of the invention.
Other mechatronic or electronic systems are for example elevators, robots, cash machines, escalators, airplanes, boats and their sub-systems. An important sub-system is for example a sensor network. Moreover, the invention can be used for data networks or telecommunication for monitoring and detecting anomalies in different sub-systems such as routers. Additionally, the inventive method enables classification of different driving scenarios and different usage of vehicles for improving service planning and up-times.
From the state of the art there are diagnosis and maintenance methods known which allow a monitoring of a single or a plurality of vehicles wherein vehicle status, service requirements, maintenance records and operational characteristics are transmitted from the vehicles to a service centre which provides diagnosis and maintenance. The collected data are compared to standard data previously defined for each vehicle. Based on detected deviations from the standard data the service centre determines whether repair, service or maintenance is necessary. The service centre is also enabled to transmit updates or to modify maintenance schedules for the vehicles.
For example, service requirements like replacement of wear parts such as engine oil, oil filters, breaking disks or windscreen wiper blades used to be defined by a certain numbers of days or a certain mileage. After expiration, the vehicle was asked to come to a repair shop for performing the defined necessary maintenance. This maintenance was performed even if the wear parts were still usable. This results in frequent stops at a repair shop, which also means that the vehicle is not usable during that time, which in turn results in increased costs due to the enforced idle period, particularly if commercial vehicles are concerned.
On the other hand, the service intervals for a vehicle for securing the availability of the vehicle have to be short in order to detect errors or faults before their occurrence. Additionally, in case a vehicle is in fact malfunctioning the search for the fault consumes a lot of time due to the plurality of different mechanical and electrical components. For reducing the diagnosis and subsequent repair time a constant monitoring of the vehicle by several sensors and transmitting the corresponding plurality of signals to the service centre would be necessary. Even with the help of wireless communication the sheer mass of data from a single vehicle to the service centre bars this possibility. Therefore, most methods use a limited set of parameters which are determined in advance to monitor the status of a vehicle and correspond to pre-selected subsystems of the vehicle. In case one parameter deviates from a previously defined standard for this parameter, the vehicle is asked to come to the repair shop for repair, maintenance or diagnosis. But in case a fault occurs in a subsystem that is not monitored, all vehicle systems have to be searched for the fault's origin, which is very time-consuming.
A further disadvantage of the methods known from the state of the art is that the standard values for the monitored parameters are defined as ideal values which are likely not to mirror the reality and are mostly not adaptable during the life time of a vehicle so that aging effects cannot be taken into account.
It is desirable to provide an improved diagnosis and maintenance method and assembly which allows for a detection and prediction of errors in a plurality of systems in short time and for optimized maintenance.
The inventive concept is, according to an aspect thereof, based on the fact that system-related signals provided by each system which describe the status of the system can or cannot show a significant relation between them. The determination whether a relation is significant or not can be performed by comparing the compatible relations between the systems. For this comparison it is preferred to use an appropriate metric. In case a significant relation is detected, this significant relation can be compared between systems, or compared for single systems over time, and used as a basis on which a decision on a necessity for providing maintenance and/or repair to an individual system can be reached. The proposed method, according to an aspect thereof, can also be updated with time, and thus adjust to e.g. wear, and can monitor subsystems that were not considered in the initial design phase. Additionally, the significant relation also relates the fault to a sub-system, whereby the time for the search for a malfunction can be reduced.
The relation can occur between same system-related signals at different time points, which allows a detection of an aging behaviour of the system, or between different system-related signals at same time-points which can be an indication for an interaction between the systems or system-parts which are characterized by the corresponding system-related signal. Additionally, it is also possible to determine a relation between different system-related signals at different time-points which can indicate an aging behaviour of the interaction between systems or system-parts.
In a preferred embodiment the relation is defined by a linear or non-linear correlation wherein also autocorrelation can be regarded. For example linear or nonlinear correlations can be represented by eigenvectors of a correlation matrix. Another preferred embodiment is the observed joint distribution of observed values, which can preferably be expressed through histogramming or clustering, wherein the latter can be with or without topological information.
Additionally, it is preferred to define a norm for the detected significant relation, wherein advantageously the norm is defined by comparing the significant relations for the plurality of systems. Further, it is preferred to detect a deviation from this defined norm. The decision whether maintenance and/or repair is necessary can then be based on the existence of a detected deviation from the norm and/or whether the detected deviation is significant.
The advantage of defining the norm by comparing the significant relation for all systems is that the reality can be taken into account. The defined norm is determined by the systems themselves under real operation conditions and not through idealised values determined e.g. under laboratory or test-bench conditions. Additionally, since it is not a priori defined which system is to be investigated but the occurrence of a significant relation between system-related signals is determined, any system can be investigated as long as there are system-related signals available that are influenced by an operation of the system.
The definition of the norm and/or the detection of a deviation from the norm are preferably performed by statistical methods, particularly statistical classification methods, wherein the significance of the deviation can be quantified by a statistical confidence test.
In a preferred embodiment the determination of a relation between system-related signals is performed by fitting a model to the signals, whereby the model, associated model parameters, a model output and a fitting quality are defined. The model captures the relations between the system-related signals and consequently represents a reduced representation of the data since, for example, only the model parameters or model outputs need to be regarded for gaining information on the system. Whether the relations are significant or not can then be determined by considering the fitting quality and the variance of the models within the plurality of systems. In addition, a cross validation of the models can be performed.
Analyzing the changes in the fitted model allows for e.g. a detection of a malfunction in a system and improving/adapting existing models provides a possibility to adapt the model to aging effects of the systems.
In a preferred embodiment, the models are fitted by the systems itself, wherein additionally the systems also perform a selection which sensors and system-related signals are monitored, and subsequently a comparison between the fitted models is performed. Based on the result of the comparison a best model or norm model can be defined which serves as basis for the decision whether maintenance or repair is necessary.
Additionally, it can be advantageous to define the norm by defining norm values for the model parameters and/or the model output, wherein for the determination of the norm values the model parameters/outputs of all systems are regarded.
In a preferred embodiment the definition of the norm values for the model parameters and/or the model output are performed by statistical methods. A simple but sufficient possibility is to calculate average values for the model parameters and/or outputs. Whether a deviation from the norm occurs can then be determined by detecting whether the model parameter/output under investigation is outside a range defined by a suitable parameterised distribution around the average value, for example given by a Gaussian probability density model.
In a further preferred embodiment the significant relation is monitored, particularly by constantly determining the model parameters and/or outputs for the significant relation and comparing the model parameters and/or outputs to the corresponding norm values, whereby a deviation from the norm values can be detected. Additionally, it is preferred to continue to adapt the model for mirroring changes, for example, due to aging effects. This is done by repeatedly fitting different models and selecting suitable models. This has the advantage that an interaction between systems can be detected that occurs only after some use or only during use in certain environments. For example the wear of a vehicle in a dry and hot environment such as a desert differs from the wear of a vehicle in humid environments. Consequently, the significant relations and their norm differ not only from the beginning but also during operation of the vehicle.
In a preferred embodiment the system performs the step of determining the relation and/or the fitting of the model itself. This results in a reduction of data volume which is transmitted to a central server, particularly a service centre, for the decision whether maintenance or repair needs to be performed. The remaining steps such as comparing the relations, determining the significant relations, detecting a deviation and deciding whether the detected deviation is significant for the need of maintenance or repair are preferably performed by the central server, which can take into account the relations of a plurality of systems. But it is also possible that the central server performs all steps and the systems just provide the system-related signals to the central server, or, vice versa, that the systems perform all steps, or that the assignment of the steps to system/central server differs.
According to another preferred embodiment, wireless communication units are provided for the communication between central server and systems.
For a further reduction of data volume it can be advantageous to send a request for the determination of a relation between predetermined system-related signals, wherein the selection between which system-related signals a relation should be determined can be performed by using random and/or deterministic search methods.
Further advantages and preferred embodiments are defined in the description and the figure.